class_docstr: The class for the computation graph based QA model.
code: def __init__(self, cfg, embed, p_graph):
      self.cfg = cfg
      self.embed = embed
      self.graph = p_graph
  
      self.query_word = None
      self.query_mask = None
      self.query_lengths = None
      self.passage_word = None
      self.passage_mask = None
      self.passage_lengths = None
      self.answer_begin = None
      self.answer_end = None
      self.query_char_ids = None
      self.query_char_lengths = None
      self.passage_char_ids = None
      self.passage_char_lengths = None
      self.passage_states = None
      self.query_states = None
      self.query_init = None
      self.begin_prob = None
      self.end_prob = None
      self.loss = None
      self.train_op = None
  
file_path: nni/examples/trials/weight_sharing/ga_squad/train_model.py
imports: import tensorflow
  from attention import DotAttention
  from rnn import XGRUCell
  from util import dropout
  from graph_to_tf import graph_to_network
label_desc: Use image node_id 1 to create a new GAG object  with example: obj = GAG(cfg, embed, p_graph)